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I just returned from an excellent conference (HEDW) which was administered by the IT staff at Cornell University. Kudos to the 2013 Conference Chair, Jeff Christen, and the staff of Cornell University for hosting this year! Below is a little bit more information about the conference:

The Higher Education Data Warehousing Forum (HEDW) is a network of higher education colleagues dedicated to promoting the sharing of knowledge and best practices regarding knowledge management in colleges and universities, including building data warehouses, developing institutional reporting strategies, and providing decision support.

The Forum meets once a year and sponsors a series of listservs to promote communication among technical developers and administrators of data access and reporting systems, data custodians, institutional researchers, and consumers of data representing a variety of internal university audiences.

There are more than 2000 active members in HEDW, representing professionals from 700+ institutions found in 38 different countries and 48 different states.

This conference has proven to be helpful to Georgetown University over the last 5 years. It is a great opportunity to network with peers and share best practice around the latest technology tools. And, sorry vendors, you are kept at bay. This is important as the focus of the conference is less on technology sales – and more about relationships and sharing successes.

Cornell University Outside of Statler Conference Center

Personally, this was my first year in attendance. I gained a lot of industry insight, but it was also helpful to find peer organizations that are using the same technology tools. We are about to embark upon an Oracle Peoplesoft finance to Workday conversion. It was helpful to connect with others that are going through similar projects. And for me specifically, it was helpful to learn how folks are starting to extract data from Workday for business intelligence purposes.

Higher Education Data Warehouse Conference

2013 HEDW Attendee List

My key take-aways from the conference were:

Business intelligence is happening with MANY tools. We saw A LOT of technology. Industry leaders in the higher education space still seem to be Oracle and Microsoft. Oracle seemed to be embedded in more universities; however many are starting projects on the Microsoft stack – particularly with the Blackboard Analytics team standardizing on the Microsoft platform. IBM Cognos still seemed to be the market leader in terms of operational reporting; however Microsoft’s SSRS is gaining momentum. From an OLAP and dashboard perspective, it seemed like a mixed bag. Some were using IBM BI Dashboards, while others were using tools such as OBIEE Dashboards, Microsoft Sharepoint’s Dashboard Designer, and an emerging product – Pyramid Analytics. Microsoft’s PowerPivot was also highly demonstrated and users like it! PowerView was mentioned, but no one seemed to have it up and running…yet. Tableau was also a very popular choice and highly recommended. Several people mentioned how responsive both Microsoft and Tableau had been to their needs pre-sale.

Business intelligence requires a SIGNIFICANT amount of governance to be successful. We saw presentation after presentation about the governance structures that should have been setup. Or, projects that had to be restarted in order be governed in the appropriate way. This includes changing business processes and ensuring that common data definitions are put in place across university silos. A stove-piped approach does not work when you are trying to analyze data cross functionally.

Standardizing on one tool is difficult. We spoke to many universities that had multiple tools in play. This is due to the difficulty of change management and training. It is worth making the investment for change management in order to standardize on the appropriate tool set.

Technology is expensive. There is no one size fits all. Depending on the licensing agreements that are in place at your university – there may be a clear technology choice. Oracle is expensive, but it may already be in use to support critical ERP systems. We also heard many universities discuss their use of Microsoft due to educational and statewide discounts available.

Predictive Analytics are still future state. We had brief discussions about statistical tools like SAS and IBM’s SPSS; however, these tools were not the focus of many discussions. It seems that most universities are trying to figure out simple ODS and EDW projects. Predictive analytics and sophisticated statistical tools are in use – but seem to be taking a back seat while IT departments get the more fundamental data models in place. Most had an extreme amount of interest in these types of predictive analytics, but felt, “we just aren’t there yet.” GIS data also came up in a small number of presentations, but also has interest. In fact, one presentation displayed a dashboard with student enrollment by county. People like to see data overlaid on a map. I can see more universities taking advantage of this soon.

Business intelligence technologists are in high demand and hard to find. It was apparent throughout the conference that many universities are challenged to find the right technology talent. Many are in need of employees that possess business intelligence and reporting skills.

Hadoop remains on the shelf. John Rome from Arizona State gave an excellent presentation about Hadoop and its functional use. He clarified how Hadoop got its name. The founder, Doug Cutting, named the company after his son’s stuffed yellow elephant! John also presented a few experiments that ASU has been doing to evaluate the value that Hadoop may be able to bring the university. In ASU’s experiments, they used Amazon’s EC2 service to quickly spin up supporting servers and configure the services necessary to support Hadoop. This presentation was entertaining, but was almost the only mention of Hadoop during the entire conference. It may have more use in research functions, but does not seem widely adopted in key university business intelligence efforts as of yet. Wonder if this will change by next year?

Great post, thanks for sharing. Data-driven decision making is making a serious impact on the future of student achievement, especially with SLDS/P-20 initiatives. Data science can be applied to many domains of knowledge; in education, the ability to identify problem areas and link important data sets in order to make better decisions for the future of our students and teachers will be important.

One more thought: a single comprehensive and timely education data standard that ensures usability is key to the success of these programs. States need to take advantage of the data analysis that is being made available, turning education data into actionable information that can change the future of our workforce.